Application of Generative Artificial Intelligence for Risk Management of Software Projects
https://doi.org/10.15514/ISPRAS-2024-36(2)-6
Abstract
The article highlights an innovative approach to risk management in software projects using generative artificial intelligence. It describes a methodology that involves the use of publicly available chatbots to identify, analyze, and prioritize risks. The Crawford method is used as a basis for risk identification. The authors propose specific formulations of requests to chatbots (instructs, prompts) that facilitate obtaining the necessary information. The effectiveness of the methodology has been demonstrated on five small software projects and a dozen of economic and organizational projects of significantly different scales, from small to federal. This confirms its applicability and practical value.
About the Authors
Anna Danielovna DZHEIRANIAN
HSE University
Russian Federation
Russian Federation
Undergraduate student at the National Research University – Higher School of Economics (HSE University, Perm Branch), educational program “Software Engineering”. Research interests: data analysis and visualization, generative models, domain-specific modeling.
Mikhail Alexandrovich PLAKSIN
HSE University, Perm State University
Russian Federation
Russian Federation
Cand. Sci. (Phys.-Math.), Associate Professor of the Department of Information Technologies in Business of the National Research University – Higher School of Economics (HSE University, Perm Branch), Associate Professor of the Department of Computer Science of the Perm State National Research University (PSU). Research interests: system analysis, TRIZ, project management, informatization of education, teaching informatics in elementary school, construction of a cross-curricular informatics course from 1st to 11th grade.
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Review
For citations:
DZHEIRANIAN A.D., PLAKSIN M.A. Application of Generative Artificial Intelligence for Risk Management of Software Projects. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2024;36(2):73-82. https://doi.org/10.15514/ISPRAS-2024-36(2)-6
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